v316: Proceedings of COMPAYL 2025

2026-06-04 · Source: Proceedings of Machine Learning Research · Field: Health & Wellbeing — Health & Medical Research, Medical Devices & Health Technology, Clinical Care & Medical Practice · Depth: Expert, medium

Summary

Volume 316 presents the proceedings of the MICCAI Workshop on Computational Pathology, held on September 27, 2025, in Marrakesh, Morocco. This collection features diverse research advancing digital pathology through artificial intelligence. Key topics include multimodal representation learning, such as the importance of text preprocessing for pathology report generation and scalable deep learning for breast cancer subtyping. Several papers focus on Whole Slide Image (WSI) analysis, covering continual learning approaches, robust tissue segmentation, and context-guided prompt learning for WSI classification. Other contributions explore generative models for predicting spatial transcriptomics from histology images (GenST) and chromosome mask-conditioned inpainting for atypical mitosis classification. The proceedings also address practical challenges like reducing site-bias batch effects from foundation models (fmMAP), enhancing interpretability via regional causal dependency discovery, and developing uncertainty-aware segmentation methods for breast cancer tissue microarrays and kidney transplant biopsies.

Key takeaway

For Machine Learning Engineers developing AI solutions in pathology, these proceedings underscore the necessity of specialized techniques. You should consider multimodal approaches, like text preprocessing for report generation, and robust WSI analysis methods, such as continual learning or uncertainty-aware segmentation. Explore foundation models, but prioritize strategies like fmMAP to mitigate site-bias batch effects. Integrating stain color augmentation can also improve domain generalization for your models.

Key insights

The workshop highlights advanced AI methods for diverse computational pathology challenges.

Principles

• Text preprocessing is crucial for multimodal pathology.
• Continual learning improves WSI analysis.
• Foundation models require bias reduction.

Method

The papers collectively explore methods including attention-based generative latent replay, spatially-aware multiple instance learning, uncertainty-aware ensemble segmentation, and hybrid state space-vision transformer backbones.

In practice

• Apply stain color augmentation for domain generalization.
• Use weakly supervised Transformers for fine-grained scoring.
• Benchmark foundation model encoders for segmentation.

Topics

• Computational Pathology
• Whole Slide Imaging
• Multimodal Learning
• Foundation Models
• Cancer Subtyping
• Image Segmentation
• Spatial Transcriptomics

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.